Deep learning recognition approaches can potentially perform better if we can extract a discriminative representation that controllably separates nuisance factors. In this paper, we propose a novel approach to explicitly enforce the extracted discriminative representation d, extracted latent variation l (e,g., background, unlabeled nuisance attributes), and semantic variation label vector s (e.g., labeled expressions/pose) to be independent and complementary to each other. We can cast this problem as an adversarial game in the latent space of an auto-encoder. Specifically, with the to-be-disentangled s, we propose to equip an end-to-end conditional adversarial network with the ability to decompose an input sample into d and l. However, we argue that maximizing the cross-entropy loss of semantic variation prediction from d is not sufficient to remove the impact of s from d, and that the uniform-target and entropy regularization are necessary. A collaborative mutual information regularization framework is further proposed to avoid unstable adversarial training. It is able to minimize the differentiable mutual information between the variables to enforce independence. The proposed discriminative representation inherits the desired tolerance property guided by prior knowledge of the task. Our proposed framework achieves top performance on diverse recognition tasks.

中文翻译：

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互信息正则化特征级科学怪人识别识别。

如果我们可以提取可控地分离出令人讨厌的因素的区分性表示，则深度学习识别方法可能会更好地发挥作用。在本文中，我们提出了一种新颖的方法来将提取的歧视性表示d，提取的潜在变异l（例如背景，未标记的讨厌属性）和语义变异标签向量s（例如标记的表达式/姿势）显式强制实施为相互独立和互补。我们可以在自动编码器的潜在空间中将此问题作为对抗性游戏。具体而言，我们建议使用待分解的s，为端到端的条件对抗网络配备能够将输入样本分解为d和l的能力。然而，我们认为，最大化d的语义变异预测的交叉熵损失不足以消除d的s的影响，并且均一目标和熵正则化是必要的。进一步提出了一种协作性的相互信息正则化框架，以避免不稳定的对抗训练。它能够最小化变量之间的可区分的互信息，以增强独立性。所提出的区分表示继承了任务的先验知识指导的期望公差属性。我们提出的框架可在各种识别任务上实现最佳性能。它能够最小化变量之间的可区分的互信息，以增强独立性。所提出的区分表示继承了任务的先验知识指导的期望公差属性。我们提出的框架可在各种识别任务上实现最佳性能。它能够最小化变量之间的可区分的互信息，以增强独立性。所提出的区分表示继承了任务的先验知识指导的期望公差属性。我们提出的框架可在各种识别任务上实现最佳性能。